An Introduction to Variational Methods for Graphical Models
Machine Learning
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Estimating a state-space model from point process observations
Neural Computation
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Convex Optimization
Dynamic analysis of neural encoding by point process adaptive filtering
Neural Computation
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Spike train decoding without spike sorting
Neural Computation
Beam sampling for the infinite hidden Markov model
Proceedings of the 25th international conference on Machine learning
Latent-Space Variational Bayes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic spike sorting using tuning information
Neural Computation
Bayesian k-Means as a "Maximization-expectation" algorithm
Neural Computation
State-space algorithms for estimating spike rate functions
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Information theory and neural information processing
IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
A new look at state-space models for neural data
Journal of Computational Neuroscience
Feature extraction from spike trains with Bayesian binning: `Latency is where the signal starts'
Journal of Computational Neuroscience
Efficient markov chain monte carlo methods for decoding neural spike trains
Neural Computation
Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes
The Journal of Machine Learning Research
Signal processing for neural spike trains
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Transfer entropy--a model-free measure of effective connectivity for the neurosciences
Journal of Computational Neuroscience
Bayesian Time Series Models
Variational Bayesian Filtering
IEEE Transactions on Signal Processing - Part II
Bayesian deconvolution of noisy filtered point processes
IEEE Transactions on Signal Processing
Bayesian Reasoning and Machine Learning
Bayesian Reasoning and Machine Learning
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
An adaptive approach to Langevin MCMC
Statistics and Computing
Machine Learning: A Probabilistic Perspective
Machine Learning: A Probabilistic Perspective
Uncovering spatial topology represented by rat hippocampal population neuronal codes
Journal of Computational Neuroscience
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Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.